Ming‐Chya Wu

403 total citations
29 papers, 319 citations indexed

About

Ming‐Chya Wu is a scholar working on Molecular Biology, Economics and Econometrics and Condensed Matter Physics. According to data from OpenAlex, Ming‐Chya Wu has authored 29 papers receiving a total of 319 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 7 papers in Economics and Econometrics and 5 papers in Condensed Matter Physics. Recurrent topics in Ming‐Chya Wu's work include Protein Structure and Dynamics (11 papers), Complex Systems and Time Series Analysis (7 papers) and Financial Risk and Volatility Modeling (5 papers). Ming‐Chya Wu is often cited by papers focused on Protein Structure and Dynamics (11 papers), Complex Systems and Time Series Analysis (7 papers) and Financial Risk and Volatility Modeling (5 papers). Ming‐Chya Wu collaborates with scholars based in Taiwan, United States and Slovakia. Ming‐Chya Wu's co-authors include Chin‐Kun Hu, N. Sh. Izmailian, Norden E. Huang, Shura Hayryan, Ján Buša, J. Skřivǎnek, K. B. Oganesyan, Jozef Džurina, Ján Plavka and Thomas C. Chiang and has published in prestigious journals such as The Astrophysical Journal, Physical Review B and Biophysical Journal.

In The Last Decade

Ming‐Chya Wu

27 papers receiving 315 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ming‐Chya Wu Taiwan 11 89 82 76 59 56 29 319
R. E. Belardinelli Argentina 8 56 0.6× 101 1.2× 194 2.6× 92 1.6× 90 1.6× 14 324
Wooseop Kwak South Korea 9 72 0.8× 77 0.9× 132 1.7× 81 1.4× 56 1.0× 28 294
Ariel Lubelski Israel 6 185 2.1× 80 1.0× 39 0.5× 131 2.2× 65 1.2× 7 457
Marco Pretti Italy 12 70 0.8× 119 1.5× 185 2.4× 77 1.3× 92 1.6× 39 407
Yiwen He China 4 164 1.8× 60 0.7× 74 1.0× 228 3.9× 91 1.6× 6 501
Tarık Çelik Türkiye 6 128 1.4× 103 1.3× 208 2.7× 83 1.4× 93 1.7× 25 346
Yukito Iba Japan 9 157 1.8× 91 1.1× 123 1.6× 68 1.2× 56 1.0× 25 374
Thomas Neusius Germany 6 172 1.9× 72 0.9× 33 0.4× 81 1.4× 79 1.4× 14 299
Aurel Jurjiu Germany 13 172 1.9× 111 1.4× 66 0.9× 105 1.8× 91 1.6× 27 488
Claire P. Massen United Kingdom 6 46 0.5× 137 1.7× 71 0.9× 201 3.4× 74 1.3× 7 390

Countries citing papers authored by Ming‐Chya Wu

Since Specialization
Citations

This map shows the geographic impact of Ming‐Chya Wu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ming‐Chya Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming‐Chya Wu more than expected).

Fields of papers citing papers by Ming‐Chya Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ming‐Chya Wu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ming‐Chya Wu. The network helps show where Ming‐Chya Wu may publish in the future.

Co-authorship network of co-authors of Ming‐Chya Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Ming‐Chya Wu. A scholar is included among the top collaborators of Ming‐Chya Wu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ming‐Chya Wu. Ming‐Chya Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Wu, Ming‐Chya, et al.. (2014). Simplified lattice model for polypeptide fibrillar transitions. Physical Review E. 90(4). 42701–42701. 3 indexed citations
2.
Wu, Ming‐Chya & Tian Yow Tsong. (2013). Local Hydrophobicity in Protein Secondary Structure Formation. Journal of the Physical Society of Japan. 82(11). 114801–114801. 4 indexed citations
3.
Wu, Ming‐Chya. (2012). Damped oscillations in the ratios of stock market indices. Europhysics Letters (EPL). 97(4). 48009–48009. 10 indexed citations
4.
Hu, Chin‐Ping, et al.. (2011). TIME-FREQUENCY ANALYSIS OF THE SUPERORBITAL MODULATION OF THE X-RAY BINARY SMC X-1 USING THE HILBERT-HUANG TRANSFORM. The Astrophysical Journal. 740(2). 67–67. 12 indexed citations
5.
Wu, Ming‐Chya, Jeffrey G. Forbes, & Kuan Wang. (2011). Cross-correlation analysis to salt-bridge dynamics in force-induced unfolding of titin kinase. 1 indexed citations
6.
Hu, Hong‐Yu, Ming‐Chya Wu, Michael Forrest, et al.. (2010). The role of tryptophan in staphylococcal nuclease stability. Biophysical Chemistry. 151(3). 170–177. 9 indexed citations
7.
Chiang, Thomas C., et al.. (2009). Statistical Properties, Dynamic Conditional Correlation, Scaling Analysis of High-Frequency Intraday Stock Returns: Evidence from Dow-Jones and Nasdaq Indices. SSRN Electronic Journal. 1 indexed citations
8.
Wu, Ming‐Chya, Eiichi Watanabe, Zbigniew R. Struzik, Chin‐Kun Hu, & Yoshiharu Yamamoto. (2009). Phase statistics approach to human ventricular fibrillation. Physical Review E. 80(5). 51917–51917. 7 indexed citations
9.
Chen, Yongzhong, et al.. (2009). Thermostability of the N-Terminal RNA-Binding Domain of the SARS-CoV Nucleocapsid Protein: Experiments and Numerical Simulations. Biophysical Journal. 96(5). 1892–1901. 6 indexed citations
10.
Chiang, Thomas C., et al.. (2008). Statistical properties, dynamic conditional correlation and scaling analysis: Evidence from Dow Jones and Nasdaq high-frequency data. Physica A Statistical Mechanics and its Applications. 388(8). 1555–1570. 9 indexed citations
11.
Wu, Ming‐Chya, et al.. (2008). Compact dimension of denatured states of staphylococcal nuclease. Proteins Structure Function and Bioinformatics. 72(3). 901–909. 4 indexed citations
12.
Hu, Chin‐Kun, et al.. (2008). Hydrophobic condensation and modular assembly model of protein folding. Biosystems. 93(1-2). 78–89. 8 indexed citations
13.
Buša, Ján, Shura Hayryan, Chin‐Kun Hu, J. Skřivǎnek, & Ming‐Chya Wu. (2008). Enveloping triangulation method for detecting internal cavities in proteins and algorithm for computing their surface areas and volumes. Journal of Computational Chemistry. 30(3). 346–357. 11 indexed citations
14.
Izmailian, N. Sh., K. B. Oganesyan, Ming‐Chya Wu, & Chin‐Kun Hu. (2006). Finite-size corrections and scaling for the triangular lattice dimer model with periodic boundary conditions. Physical Review E. 73(1). 16128–16128. 30 indexed citations
15.
Wu, Ming‐Chya, et al.. (2006). Phase distribution and phase correlation of financial time series. Physical Review E. 73(1). 16118–16118.
16.
Wu, Ming‐Chya. (2006). Phase Statistics Approach to Time Series Analysis. 2 indexed citations
17.
Allahverdyan, A. E., et al.. (2004). Unzipping of DNA with correlated base sequence. Physical Review E. 69(6). 61908–61908. 12 indexed citations
18.
Wu, Ming‐Chya, Chin‐Kun Hu, & N. Sh. Izmailian. (2003). Universal finite-size scaling functions with exact nonuniversal metric factors. Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 67(6). 65103–65103. 34 indexed citations
19.
Wu, Ming‐Chya & Chin‐Kun Hu. (2002). Exact partition functions of the Ising model on M $times$ N planar lattices with periodic$ndash$aperiodic boundary conditions. Journal of Physics A Mathematical and General. 35(25). 5189–5206. 27 indexed citations
20.
Huang, Ming-Chang & Ming‐Chya Wu. (1998). The Caldirola-Kanai Model and Its Equivalent Theories for a Damped Oscillator. Chinese Journal of Physics. 36(4). 566. 6 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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